Multiphase level set with multi dynamic shape models on kidney segmentation of CT image

Huang, Yao-Pin; Chung, Pau‐Choo; Huang, Chieh-Ling; Huang, Chun-Rong

doi:10.1109/biocas.2009.5372065

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…Conventional speed functions accounting for image intensities, object edges, gradient vector flow, etc., are unsuccessful on very noisy images with low object-background intensity gradients. The results are improved by involving shape priors (e.g., [5,6,11]). To obtain more accurate results, our stochastic speed function accounts for both the shape prior and appearance features associated with image intensities and their spatial interactions integrated into a 3-D two-level joint MGRF model.…”

Section: The Proposed Level Set-based Segmentation Approachmentioning

confidence: 99%

“…Recently, Huang et al [5] proposed a multiphase level set approach with multi-dynamic shape models to segment the kidneys on abdominal CT images. Spiegel et al [6] proposed a kidney segmentation framework based on the active shape model (ASM) that was combined with a curvature-based non-rigid registration approach to solve the point correspondence problem of the training data.…”

Section: Introductionmentioning

confidence: 99%

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3D Kidney Segmentation from CT Images Using a Level Set Approach Guided by a Novel Stochastic Speed Function

Khalifa

Elnakib

Beache

et al. 2011

Lecture Notes in Computer Science

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Kidney segmentation is a key step in developing any noninvasive computer-aided diagnosis (CAD) system for early detection of acute renal rejection. This paper describes a new 3-D segmentation approach for the kidney from computed tomography (CT) images. The kidney borders are segmented from the surrounding abdominal tissues with a geometric deformable model guided by a special stochastic speed relationship. The latter accounts for a shape prior and appearance features in terms of voxel-wise image intensities and their pair-wise spatial interactions integrated into a two-level joint Markov-Gibbs random field (MGRF) model of the kidney and its background. The segmentation approach was evaluated on 21 CT data sets with available manual expert segmentation. The performance evaluation based on the receiver operating characteristic (ROC) and Dice similarity coefficient (DSC) between manually drawn and automatically segmented contours confirm the robustness and accuracy of the proposed segmentation approach.

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Section: The Proposed Level Set-based Segmentation Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D Kidney Segmentation from CT Images Using a Level Set Approach Guided by a Novel Stochastic Speed Function

Khalifa

Elnakib

Beache

et al. 2011

Lecture Notes in Computer Science

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“…This methodology was extended by Markov-Gibbs Random Field to model the kidney and surrounding tissues [12]. Huang et al [13] proposed a modification of the Chan-Vese model [14] by using a shape model with kidney variation. Similarly to the methods presented in Section 2, this kind of solution also requires a big database with doctors' manual outlines.…”

Section: Introductionmentioning

confidence: 99%

Kidney Segmentation in CT Data Using Hybrid Level-Set Method with Ellipsoidal Shape Constraints

Skalski

Heryan

Jakubowski

et al. 2017

Metrology and Measurement Systems

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With development of medical diagnostic and imaging techniques the sparing surgeries are facilitated. Renal cancer is one of examples. In order to minimize the amount of healthy kidney removed during the treatment procedure, it is essential to design a system that provides three-dimensional visualization prior to the surgery. The information about location of crucial structures (e.g. kidney, renal ureter and arteries) and their mutual spatial arrangement should be delivered to the operator. The introduction of such a system meets both the requirements and expectations of oncological surgeons. In this paper, we present one of the most important steps towards building such a system: a new approach to kidney segmentation from Computed Tomography data. The segmentation is based on the Active Contour Method using the Level Set (LS) framework. During the segmentation process the energy functional describing an image is the subject to minimize. The functional proposed in this paper consists of four terms. In contrast to the original approach containing solely the region and boundary terms, the ellipsoidal shape constraint was also introduced. This additional limitation imposed on evolution of the function prevents from leakage to undesired regions. The proposed methodology was tested on 10 Computed Tomography scans from patients diagnosed with renal cancer. The database contained the results of studies performed in several medical centers and on different devices. The average effectiveness of the proposed solution regarding the Dice Coefficient and average Hausdorff distance was equal to 0.862 and 2.37 mm, respectively. Both the qualitative and quantitative evaluations confirm effectiveness of the proposed solution.

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“…10 Even this combined approach may fail in the face of high anatomic variation, and several groups have therefore chosen to employ a priori anatomic information. Huang supplements the Chan-Vese method with a model built from training sets, 11 while Lin adds a model to a region growing method. 12 Freedman and Ali each employ models from training sets in conjunction with a graph cuts approach.…”

Section: Introductionmentioning

confidence: 99%

“…12 Freedman and Ali each employ models from training sets in conjunction with a graph cuts approach. 13,14 While some research utilizes a fully automated methodology, [11][12][13] fully automating such a variable process can be difficult, and some groups prefer to leave some user interaction intact. 15,16 Such diverse approaches lead to a wide range of execution times.…”

Section: Introductionmentioning

confidence: 99%

Comparison and assessment of semi‐automatic image segmentation in computed tomography scans for image‐guided kidney surgery

et al. 2011

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Use of the algorithm shortened user interaction time and provided results which were comparable to the gold standard of hand segmentation. Further, the use of the algorithm's resultant surfaces in image registration provided comparable transformations to surfaces produced by hand segmentation. These data support the applicability and utility of such an algorithm as part of an image guidance workflow.

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Multiphase level set with multi dynamic shape models on kidney segmentation of CT image

Cited by 11 publications

References 16 publications

3D Kidney Segmentation from CT Images Using a Level Set Approach Guided by a Novel Stochastic Speed Function

3D Kidney Segmentation from CT Images Using a Level Set Approach Guided by a Novel Stochastic Speed Function

Kidney Segmentation in CT Data Using Hybrid Level-Set Method with Ellipsoidal Shape Constraints

Comparison and assessment of semi‐automatic image segmentation in computed tomography scans for image‐guided kidney surgery

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